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Lookup NU author(s): Dr Steven Hancock
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Snow exerts a strong influence upon weather and climate. Accurate representation of snow processes within models is needed to ensure accurate predictions. Snow processes are known to be a weakness of land surface models (LSMs) and studies suggest that more complex snow physics is needed to avoid early melt (Blyth et al. 2010; IPCC 2007). In this study ESA’s Globsnow snow water equivalent (Takala et al. 2011) and NASA’s MOD10C1 snow cover (Hall et al. 2006) products are used to assess the accuracy of snow processes within the Joint UK Land Environment Simulator (JULES) (Best et al. 2011; Clark et al. 2011). JULES is run “offline” from the circulation model and so is driven by meteorological re-analysis datasets: Princeton, WATCH-GPCC and WATCH-CRU (Sheffield et al. 2006; Weedon et al. 2010). This reveals that when the model achieves the correct peak accumulation, snow does not melt early. However, generally snow does melt early because peak accumulation is too low.Examination of the meteorological re-analysis data shows that not enough snow falls to achieve observed peak accumulations. Thus the earlier studies’ conclusions may be due to weaknesses in the driving data rather than in model snow processes. These re-analysis products “bias correct” precipitation using observed gauge data (Sheffield et al. 2006; Weedon et al. 2010) with an undercatch correction, over-writing the benefit of any other datasets used in their creation. We argue that using gauge data to bias correct re-analysis data is not appropriate for snow-affected regions during winter and can lead to confusion when evaluating model processes.
Author(s): Hancock S, Huntley B, Ellis R, Baxter R
Publication type: Article
Publication status: Published
Journal: Journal of Climate
Print publication date: 01/01/2014
ISSN (print): 0894-8755
ISSN (electronic): 1520-0442
Publisher: American Meteorological Society
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